The role of Data Science in Targeted Advertising

1. By
Name:SHIVAKUMAR
Reg no: 2237019
Class: 2MAECO
Submission Date: 05/04/20203

2. Topic
The Application of Data Science In
Targeted Advertisement

3. contents
 Introduction to Data Science And
 Targeted Advertising
 The issues of Advertising
 Data Science Approach to the issues
 The Application and use of Data Science
In Targeted Advertising
 Conclusion

4. What is Data Science?
Data science is a multi-disciplinary field that uses scientific method
processes, algorithms and systems to extract knowledge and insight
from structured and unstructured data.

5. What Is Targeted Advertising?
Targeted advertising is a form of online advertising that is directed
towards audiences with certain traits, based on the product or
person the advertiser is promoting.
Traints can be:
 Demographics:race, economic status, sex, age, the level of educat
income level and employment
 Psychographic: consumer’s values, personality, attitudes, opinion
lifestyles and interests.
 behavioral variables: browser history, purchase history, and
other recent activities.

6. Challenges in Creating Targeted Ads
Consumer concerns over privacy:
Two out of three consumers (opens outside ibm.com) want ads that are
personalized to their interests. Yet, nearly half of consumers are
uncomfortable with sharing their data to create personalized ads.
Additionally, 65% (opens outside ibm.com) of consumers worry that brands
are collecting personal information without their permission. Seventy-four
percent (opens outside ibm.com) also think that companies are collecting
more information than they need.
Data Maintainance issues:
Data needs to be continuously updated to ensure accuracy. For companies serious about
taking a data-driven approach, there must be a process in place to validate existing
information and to ensure the data is clean, correct and usable.

7. Inaccurate data :
If data isn’t reliable, it is difficult for teams to pivot their plan or determine which
strategies are effective. For this reason, 91% (opens outside ibm.com) of
from an Experian study noted that data quality is a necessary part of creating
a data-driven culture in the workplace.
Data collection Regulation:
For companies looking to leverage data, it’s important to understand what limits
these regulations place on your efforts. Companies might have to store data in a
specific way or may not be allowed to send unsolicited emails
Weather targeting:
Weather plays a big role in decision-making because it has such a profound impact on
the decisions people make. By leveraging accurate weather forecasting information,
brands can reach consumers at the right time.

8. Data needed for Targeted Advertising:
• The content of a web page
• Weather data
• Location data
• Behavioral signals
Device
Data science approaches to these challenges
Data science, machine learning, and AI can be used in various ways to reach a target
audience. These include:
Contextual ads
Contextual ads use various data signals, including the content of a page, weather signals
and location to determine the right target audience. Instead of relying on third-party
cookies, AI is able to leverage these factors to determine the best time to serve an ad
to an end user.

9. Predictive targeting:
Companies can easily identify their current customers, but may face more
challenges when finding new audiences. Predictive targeting uses insights obtained
from data science to determine when ads should be displayed to a user.
Dynamic creative optimization:
Dynamic creative optimization (DCO) is able to determine which message will best
engage users based on various information, including device, location, weather and date.

10. Conclusion:
Data science can be a great tool to deliver more targeted ads to end users without
the use of cookies. By leveraging machine learning and AI, advertisers can ensure
the right message is reaching the right audience at the right time